1. Field of Invention
This invention relates to a tomograph which has the capability of operating in either X-Ray computerized tomography (CT) or positron emission tomography (PET) mode. More specifically, it relates to a combined PET and CT scanner which allows co-registered CT and PET images to be acquired sequentially in a single device, overcoming alignment problems due to internal organ movement, variations in scanner bed profile, and positioning of the patient for the scan.
2. Description of the Related Art
The role of PET imaging in oncology research and patient care is growing. The ability of PET to add unique functional information to that obtained by conventional anatomical-based modalities, such as CT and magnetic resonance (MR), is generating considerable interest. For space-occupying lesions in the head, chest, abdomen and pelvis, one of the best documented applications of PET is in the discrimination of benign from malignant causes. Thus far, 18F-fluorodeoxyglucose (FDG) has been used to image the distribution of glucose uptake in all of these applications. The increased glucose metabolism of the neoplasm has been used for several purposes. Specific applications include, among other things, determining the presence of recurrent glioma versus radiation necrosis, determining the presence of recurrent colon carcinoma versus surgical scar and radiation changes, determining the presence of pancreatic cancer versus pancreatitis, determining the presence of malignant solitary pulmonary nodules versus benign nodules, and determining the presence of metastatic lung carcinoma versus reactive lymph node.
Some centers are investigating the use of quantitative analysis to increase specificity of FDG uptake. Others are expanding the tumor types that can be characterized. In addition, the development of other radiotracers which image different aspects of tumor metabolism and growth add a further dimension to this research activity. These tracers include 11C-methionine to measure amino acid incorporation, 18F-thymidine to measure nucleotide incorporation (a measure of cell proliferation), and 18F-fluoromisonidazole to measure tissue hypoxia. These alternative imaging possibilities have prompted new investigations to determine whether physiological changes early after chemotherapy or radiation treatment can be seen by PET and used to provide predictions of tumor response.
Another increasingly important application of FDG in oncology is for whole-body scans. Using this technique to stage cancer, occult metastatic disease in almost any region of the body can potentially be detected by increased FDG accumulation. The sensitivity of this approach to small lesions, however, is unclear and may depend on accurate transmission information which is often time-consuming to obtain in whole-body mode.
Finally, the PET imaging of tumor masses, and particularly complex tumor masses with areas of cystic changes, necrosis, or surrounding edema, could potentially be used to guide diagnostic biopsies. In the head, this has been demonstrated to be fairly successful, but extracranial applications have not yet been studied systematically. While accuracy and reliability of CT-guided biopsies is high overall, typically greater than ninety percent (90%), it is known that this accuracy and reliability falls considerably to approximately eighty percent (80%) in the setting of complex lesions in presacral or retroperitoneal locations. Thus, functional knowledge of tumor metabolism would be helpful in better selecting an exact biopsy site in these conditions if correctly registered to CT data.
In recent years, there has been considerable progress in the development of techniques to co-register and align functional and anatomical images. This has been driven primarily by the demand for accurate localization of cerebral function visualized in PET studies where the low resolution morphology is, in most cases, insufficient to identify the related cerebral structures. Techniques to overcome this problem have been developed based, for example, on the identification of certain geometrical features common to both imaging modalities. For example, A. C. Evans et al., J Cereb Blood Flow Metab 11(2), A69-A78 (1991) teach the use of landmark matching while D. G. Thomas et al., “Use of relocatable stereotactic frame to integrate positron emission tomography and computed tomography images: application to human malignant brain tumors,” Stereotactic and Functional Neurosurgery 54-55, 388-392 (1990) teach the use of externally-placed reference or fiducial markers. Identification of the skull and brain contour from either the PET transmission or emission scan and the MR or CT scan has also been employed as an alignment technique by C. A. Pelizzari et al, “Accurate three-dimensional registration of CT, PET and MR images of the brain,” J Comp Assist Tomogr 13, 2026 (1989). Following the identification of common structures in the two modalities, a rigid-body transformation is used to rotate and translate the MR or CT scan into the reference frame of the PET image, accounting for differences in pixel size between the two imaging modalities. A technique which uses a least squares approach to minimize the distribution of pixel-to-pixel ratios between the two images requiring alignment has proved successful both for PET to PET by R. P. Woods et al., “Rapid automated algorithm for aligning and reslicing PET images,” J Comp Assist Tomogr 16, 620-633 (1992); and PET to MR by R. P. Woods et al., “MRI-PET registration with an automated algorithm,” J Comp Assist Tomogr 17, 536-546 (1993). An interactive method has also been published by U. Pietrzyk et al., “Three-dimensional alignment of functional and morphological tomograms,” J Comp Assist Tomogr 14(1), 51-59 (1990), wherein a human observer makes alignment decisions based on visual inspection of images of brain sections displayed on a computer screen.
After two images from different modalities are aligned, they can be displayed in a number of ways, such as, for example, side by side with linked cross-hair cursors so that positional correspondence between the two image sets is easily established. This type of software tool is now readily available commercially. A different technique that is more appropriate for this project is that of image fusion, in which the two different image sets are combined into a single image so that positional correspondence is automatically established. Fusion techniques in general consist of either statistical methods or color-wash methods. Color-wash methods assign a color scale to one image and an intensity scale to the other image, whereas statistical methods select the most significant values from each image and assign as many orthogonal colors to each as possible for the particular display device.
Essentially all the registration techniques mentioned above have been developed for use in cerebral studies, and in particular brain activation. This is to some extent because PET images of cerebral flow and metabolism already contain a limited amount of low-resolution anatomical information which can be effectively exploited by the alignment procedures. However, the problems of alignment and co-registration in other regions of the human body are more difficult to solve owing to the absence of even low-resolution morphology in the functional image. This is particularly acute in the abdomen, where the PET emission scan shows little or no anatomical detail. Furthermore, the advantage of co-registering organs other than the brain has been recognized only recently, with, as described above, a rapid growth in the use of FDG in oncology.
It is evident, therefore, that in regions such as the thorax and abdomen, the demonstration of increased FDG uptake is limited in value without an unambiguous localization of tracer uptake to a specific structure (e.g., a tumor) seen on the corresponding CT image. It is desirable, therefore, to accomplish accurate registration of anatomical data, such as is obtained with CT, to improve the use of PET imaging in all of the above applications in oncology. In the discrimination of a benign versus a malignant mass, a CT scan typically defines the borders of the mass and co-registration with PET allows a more accurate quantitative evaluation. In certain organs, where nearby structures have a high concentration of excreted tracers, such as FDG in the renal pelvis, exact registration of PET and CT allows a finer discrimination of the etiology of a “hot spot”, thus reducing the likelihood of falsely identifying the mass as a tumor, or misjudging a focal accumulation of tracer as probable urine activity. For future tracers which may have labeled metabolites excreted via the hepatobiliary system and bowel, this may be even more crucial.
In “‘Anatometabolic’ tumor imaging: fusion of FDG PET with CT or MRI to localize foci of increased activity,” J. Nucl. Med. 34, 1190-1197 (1993), R. L. Wahl et al., disclose the alignment of CT scans with PET FDG functional images and have thus demonstrated the importance of combining anatomy and function in organs other than the brain. Wahl et al., concentrated on tumors in the thoracic and abdominal regions using both external markers and, in the thorax, internal anatomical landmarks such as the carina. Functional and anatomical images were aligned to within an error of magnitude of 5-6 mm, allowing more precise information to be obtained on the extent of the tumoral involvement of surrounding soft tissues than would have been possible from the PET scan alone. This work has also highlighted the difficulties of aligning organs which are not rigidly attached within the body. While the brain remains fixed in the skull, the position of organs such as the liver may depend upon the precise way in which the patient lies on the bed. Thus, PET-CT post-hoc alignment may be affected by different internal relationships and deformations within the body, limiting the accuracy of such an approach.
As is well-known, compared to anatomical imaging modalities, SPECT images are photon-limited and generally lack anatomical landmarks, thus making image alignment, and the definition of regions-of-interest, even more of a problem than it is for PET. In addition, non-uniform photon attenuation introduces distortions and artifacts into the reconstructed images. A prototype hybrid CT/SPECT scanner has been developed to address these issues. As discussed by T. F. Lang et al., “A prototype emission-transmission imaging system,” IEEE Nucl. Sci. Symposium Conf. Record 3, 1902-1906 (1991); and T. F. Lang et al., “Description of a prototype emission-transmission computed tomography imaging system,” J. Nucl. Med. 33, 1881-1887 (1992), this device employs the same one-dimensional array of high-purity germanium detectors for both CT and single photon imaging. A goal of the CT/SPECT project is also to use the X-ray CT image to provide the attenuation factors to correct the SPECT data, as suggested by J. S. Fleming, “A technique for using CT images in attenuation correction and quantification in SPECT,” Nucl. Med. Commun 10, 83-97 (1989). The use of CT images for attenuation correction had been originally proposed by S. C. Moore, “Attenuation compensation” in Ell, P. J. et al., Computed Emission Tomography, London, Oxford University Press, 339-360 (1982). The 100 kVp X-ray source is capable of producing a dual-energy X-ray beam, such that an energy-corrected attenuation map can be obtained for use with the radionuclide data, as disclosed by B. H. Hasegawa et al., “Object specific attenuation correction of SPECT with correlated dual-energy X-ray CT,” IEEE Trans. Nucl. Sci. NS-40 (4), 1242-1252 (1993). Operating the device with two energy windows also allows simultaneous emission-transmission acquisitions to be performed, although the authors report a certain level of contamination of the emission scan by the transmission X-ray beam. This disclosure demonstrates the potential of a device capable of performing both anatomical and functional measurements. It has also given rise to a detailed simulation study to investigate the different techniques for scaling the attenuation coefficients from CT energies (50-80 keV) to SPECT (140 keV). See K. J. LaCroix et al., “Investigation of the use of X-ray CT images for attenuation compensation in SPECT,” IEEE 1993 Medical Imaging Conference Record (1994).
While the attenuation correction for PET is of a greater magnitude than for SPECT, it is theoretically more straightforward. However, since it is generally based on patient measurements (a transmission scan), it introduces additional noise into the reconstructed emission scan. In practice, in order to limit the duration of the PET scan procedure, abdominal transmission scans of 10-15 minutes are typical, during which 100 million counts are acquired (3 million per slice, or 100 counts per coincidence line of response, i.e. a 10% statistical accuracy), which introduces significant noise into the corrected emission scan. In practice, only lines-of-response (LOR's) through the patient contain useful transmission information, and since some of the coincidence events will lie in LOR's which do not pass through the patient, the total useful counts in a transmission scan is often less than 100 million. In addition, patient movement between the transmission and emission scan (which may be acquired 40 minutes or so later) can introduce serious artifacts and distortions into the reconstructed image, as disclosed by S. C. Huang et al., “Quantitation in positron emission tomography: 2. Effects of inaccurate attenuation correction,” J Comput Assist Tomogr 3, 804-814 (1979).
FIGS. 1A and 1B illustrate a PET transmission image and a CT image, respectively, for the same transaxial section through a patient. As illustrated in these figures, the statistical noise in a CT image is considerably less than that in an image formed from a PET transmission scan, due to the much higher photon flux available in CT scans. A CT image is formed from a photon flux equivalent to 1010-1011 photons, compared with the ˜106 photons/slice in a PET transmission scan.
In a typical PET transmission scanning procedure, the PET transmission scan is performed pre-injection, while the emission scan is performed 45 minutes post-injection, which is potentially a significant source of error if there is any patient motion during this period.
In the above, only the effect of statistical noise introduced into the emission scan by the finite statistics of the transmission scan has been considered. However, there is also an important systematic component to the noise due to the level of scatter in the transmission scan. In a full-ring scanner with rotating rod sources, the position of the sources are monitored and only detected coincidence events which pass close to one of the rods are accepted. W. F. Jones et al., in “Optimizing rod window width in positron emission tomography,” IEEE 1992 Medical Imaging Conference Record 2, 982-984 (1993), disclose this procedure, which is known as rod windowing. This eliminates much of the scatter, with the possible exception of very small angle scatters. Scatter contamination in the transmission scan results in an underestimate of the linear attenuation coefficients, and a consequent under-correction of the emission scan. In the brain, the tissue attenuation coefficient is underestimated by 12% compared with the known value for 511 keV photons in water.
R. A. de Kemp et al., in “Attenuation correction in PET using single photon transmission measurement,” Med. Phys. 21, 771-778 (1994); and Qu He et al., in “Attenuation correction in PET using a singles transmission source,” J Nucl Med 35 (5), 41P (1994), performed studies using an orbiting point source of 511 keV photons for transmission scanning. In this approach only singles are detected, with the position of the source providing the second point on the line-of-response. The statistics in the transmission scan is greatly increased due to the high singles/coincidence ratio, and the fact that the rate is limited by the deadtime of the detector close to the source. However, this study fails to address the problem of scatter in the singles transmission scan for quantitative PET imaging.
The PET attenuation correction factor exp{∫dtμE(t)} is the line integral of the linear attenuation coefficient for each coincidence channel, where E is the photon energy and t is the spatial coordinate along the channel. The attenuation coefficient μE(x) is energy-dependent, and determination of these factors at one energy requires scaling if they are to be used to correct emission data at a different energy. There are two difficulties with scaling CT attenuation factors for use with PET data. Specifically, the annihilation photons used by PET are monoenergetic 511 keV whereas the X-ray source used in CT emits photons which cover a relatively broad spectrum from 40 keV to 120 keV. Second, the attenuation at CT-energies is a combination of both Compton scattering and photoelectric absorption, while at 511 keV the contribution of photoelectric absorption even in bone is essentially negligible.
Solutions to these problems for SPECT applications has been reported, focusing on two approaches to transform μE(x) from the lower energy at which it is measured to the higher energy at which it is required. The first approach, investigated in simulations by LaCroix et al. (1994), scales μE(x) from an effective CT photon energy level in the range of 50-80 keV up to SPECT photon energy level of 140 keV by using a single scaling factor given by the ratio of the attenuation for water at the two energies. While this is a good approximation when the major contribution to μE(x) comes from Compton interactions, it is, however, a poor approximation when photoelectric contributions dominate, as they do at CT energies. The error is particularly large for higher Z materials, such as bone, which contains a large percentage of calcium.
The second and more technically-challenging approach, is to acquire the CT image at two different photon energies—for example, 40 keV and 80 keV—and use these data to extract the individual photoelectric and Compton contributions to μE(x). See R. E. Alvarez et al., “Energy-selective reconstructions in X-ray computerized tomography,” Phys Med Biol 21(5), 733-744 (1976); and D. E. Avrin et al., “Clinical applications of Compton and photo-electric reconstruction in computed tomography: preliminary results,” Invest Radiol 13, 217-222 (1978). The different contributions are then scaled in energy separately. The Compton contribution decreases linearly while the photoelectric contribution decreases rapidly as 1/E3. The two separate contributions can be scaled independently and combined to form a monoenergetic attenuation map at 140 keV as shown by Hasegawa et al., for a prototype SPECT/CT detector block. Dual-energy CT is an accurate technique for determining the Compton and photoelectric contributions in these energy ranges, but the extrapolation of the monoenergetic attenuation map to 511 keV is not readily apparent. Additionally, the formation and detection of two CT spectra is technically challenging, requiring either the mechanical switching of foil filters, or the switching of the X-ray tube accelerating voltage, which is limited by the possibility of overheating. It also generally requires a complex calibration procedure.
The development of 3D PET has resulted in the acquisition of PET data with a significant fraction of scattered events. This is because the role of the septa, which are retracted during a 3D acquisition, is primarily to shield the detectors from out-of-plane scatter. The absence of shielding is reflected in a factor of three increase in scatter, from 10%-15% of the total events collected in 2D with septa extended, to 40%-45% in 3D with the septa retracted. This increase has served to focus attention on the problem of scatter correction in PET. However, even in 2D, for accurate quantitation, the scatter background of 10%-15% must be subtracted. The non-negligible scatter contribution in 2D PET comes mainly from in-plane scatter, and is a consequence of the poor energy resolution of the BGO block design. Loss of light in the block results in a decrease in energy resolution from an intrinsic 11% to as much as 23% in a block design. PET scanners are therefore operated with a lower energy threshold set typically between 250 keV and 350 keV to minimize the rejection of unscattered photons. At such a threshold, photons which lose only a small amount of energy by scattering, and are hence deviated through a small angle, are accepted as true, photopeak events. Application of an energy threshold therefore results in the preferential selection of the more forward-peaked component of the energy spectrum.
The consequence of one or both coincidence photons scattering before reaching the detector is a mispositioning of the event into a different, incorrect, LOR. However, attenuation correction in PET assumes that events have been removed from LOR's. This is accomplished either by photoelectric absorption, which is a negligible effect at 511 keV, or by Compton scattering, and not by simply being repositioned from one correct LOR to another. Therefore, the correction for mispositioning, or scatter correction, must precede correction for attenuation. Otherwise, the attenuation correction, which is an exact procedure in PET, will also be applied to the mispositioned events. This effectively distorts and amplifies the effect of scatter, making an effective scatter correction considerably more complex.
Unlike scatter in single photon tomography (SPECT), scattered events in PET may be mispositioned to LOR's outside the body. This observation has led to a simple scatter correction technique that is reasonably effective in correcting for scatter within the brain. In this approach, the distribution of the mispositioned events outside the brain is used to estimate the scatter background within the brain. While this procedure may be reasonably effective for the brain and at the low levels of scatter encountered in 2D PET, it is not satisfactory for determining scatter in other parts of the body, or in handling the increased amount of scatter in 3D. More accurate 2D scatter correction techniques have been developed by, for example, M. Bergström et al., “Correction for scattered radiation in a ring detector positron camera by integral transform of the projections,” J. Comput. Assist. Tomogr. 7(1), 42-50 (1983); and M. Endo et al., “Software correction of scatter coincidence in positron CT,” Eur. J. Nucl. Med. 9, 391-396 (1984), based on integral transform methods. This approach models scatter as a spatially-invariant convolution of the unscattered projection data with a kernel determined from line source or point source measurements in a uniform phantom. Since the scatter background depends on both the tracer distribution and the density distribution of the scattering medium, the convolution approach has only a limited potential to account for such spatially-variant effects.
In 3D, the increased amounts of scatter demand an accurate correction procedure to improve contrast, without losing the quantitation which is a unique feature of PET. S. R. Cherry et al., “Correction and characterization of scattered events in three dimensional PET using scanner with retractable septa,” J Nucl Med 34, 671-678 (1993), have proposed the use of an auxiliary 2D scan from which the scatter distribution in 3D can be estimated. Following the Bergström-type approach, D. L. Bailey et al., “A convolution-subtraction scatter correction method for 3D PET,” Phys Med Biol 39, 412-424 (1994), investigated a convolution-subtraction method to correct for scatter. The scatter kernel is modeled as a mono-exponential function of the form k(−b|x|), where k is the scatter ratio (scatter/trues) and b is the slope of the tails of the scatter distribution. The parameters k and b are obtained from line source measurements in an appropriate phantom (e.g. a 20 cm diameter uniform cylinder), and x is then the distance from the line source. The procedure is iterative, such that in the first step the scatter is obtained from the convolution of the measured projection data with the scatter kernel. This scatter estimate is then subtracted from the measured projections. However, this first step will tend to overcorrect because the measured projection data used in the convolution includes scatter. In subsequent iterations, the kernel is convolved with the measured projection data after subtraction of scatter estimated from the previous step. Three to four iterations are typically sufficient. A recent improvement to this method (Bailey et al.) has been the introduction of a spatially-variant scatter ratio (k(x)), based on geometrical information obtained from the PET transmission scan. This approach may therefore be improved by use of the CT scan rather than the PET transmission scan.
A second approach taken by D. Gagnon et al., “Introduction to holospectral imaging in nuclear medicine for scatter subtraction,” IEEE Trans Med Imaging 8(3), 245-250 (1989), which has been evaluated for scatter correction in 3D PET, is based on the use of multiple energy windows, a procedure that has its origins in single photon tomography. In this approach, data is simultaneously collected in more than one energy window, and the information from other windows is used to estimate the scatter within the photopeak window. State-of-the-art PET scanners have the capability to acquire data in two energy windows, and two different implementations of a dual energy window scatter correction have been proposed. The first, studied by S. Grootoonk et al., “Correction for scatter using a dual energy window technique with a tomograph operating without septa.” IEEE 1991 Medical Imaging Conference Record, 1569-1573 (1992), sets a lower energy window which is assumed to contain predominantly scatter. Data collected in this lower window are then scaled to provide an estimate of the scatter contribution in the photopeak window, where the scaling factors are obtained from line source measurements in air and phantoms. A second dual-energy window approach, the Estimation of Trues Method due to B. Bendriem et al., “A PET scatter correction using simultaneous acquisitions with low and high lower energy thresholds,” IEEE 1993 Medical Imaging Conference Record 3, 1779-1783 (1994), sets an upper window with a very high (650 keV) lower threshold such that it contains only true coincidences. Again, the information in this window is used to estimate the contribution of true coincidences in the photopeak window. This approach has the advantage that the estimate in the upper window does not depend upon the scattering medium since it contains only true coincidences. Statistical noise may be a problem, however, due to the small number of counts collected in this upper window. In contrast to the convolution-subtraction method, the dual-energy window approach can in principle take into account scatter from activity outside the field-of-view, at least to the extent to which this information is contained in the lower energy window. For the method of Grootoonk et al., efforts are also being made to incorporate spatial information based on the PET transmission scan.
A third method investigated by J. M. Ollinger is to directly model the scatter using the Klein-Nishina equation describing Compton scattering. This method is disclosed in Ollinger, J. M., “Model-based scatter correction for fully 3D PET,” Phys Med Biol. 41(1), 153-176, (1996). In this approach, the tracer distribution is obtained from a reconstruction uncorrected for scatter, and the geometry of the scattering medium is obtained from the PET transmission image. The expected distribution of scatter in the projections is then calculated using the Klein-Nishina equation which gives the probability of a photon scattering through a particular angle. Such an approach obviously involves considerable computational effort, and an efficient implementation has been developed by Ollinger which is capable of estimating the distribution of scatter in the projections within a few minutes on a fast processor. Detailed Monte Carlo simulations of scatter yield a generally low-frequency background with few features of the underlying tracer distribution or scattering medium. Therefore, it is sufficient to model such a slowly-varying distribution at a lower spatial resolution than the measured projection data, thereby considerably reducing computation time. Again, since this method has no information concerning activity outside the field-of-view, the calculated scatter distribution will not in general correct for such an eventuality. Watson et al., have independently developed a similar approach to Ollinger based on a single scatter model, as disclosed in Watson, C. C., D. Newport, and M. E. Casey, “A single scatter simulation technique for scatter correction in 3D PET” in: Grangeat P and J-L Amans, Three Dimensional Image Reconstruction in Radiology and Nuclear Medicine., Dordrecht: Kluwer Academic. 255-268 (1996).
Thus, the techniques, both 2D and 3D, which have been developed to correct for scatter in PET may be summarized as (1) profile subtraction based on extrapolating the background level from outside the object; (2) integral transforms on 1D projection data; (3) estimating 3D scatter from a measurement of scatter in 2D; (4) iterative convolution-subtraction with a spatially-variant scatter fraction; (5) dual energy window, including the ETM approach; and (6) a model-based method in which the scatter contribution is calculated. Other approaches, such as the use of multiple energy windows are not possible with this scanner design. All methods for scatter correction in 3D PET have demonstrated a certain amount of success by reducing scatter to a level below that generally accepted for 2D PET. However, the accuracy of techniques described above can certainly be improved by incorporating geometrical information more precise than that currently available from a PET transmission scan.
One possibility that arises when anatomical CT data is accurately co-registered with functional PET data is to use the CT image to constrain the PET image reconstruction. A maximum a posteriori (MAP) method has been explored with co-registered PET and MR images by R. Leahy et al., “Incorporation of anatomical MR data for improved functional imaging with PET,” Information Processing in Medical Imaging, XIIth IPMI International Conference, Wye, UK, 105-120. (1991); X. Yan et al., “MAP estimation of PET images using prior anatomical information from MR scans,” IEEE 1992 Medical Imaging Conference Record 2, 1201-1203 (1993); and others. Promising results from simulation studies by Z. Zhou, et al., “A comparative study of the effects of using anatomical priors in PET reconstruction,” IEEE 1993 Medical Imaging Conference Record 3, 1749-1753 (1994), show that incorporation of prior anatomical boundary information into the MAP reconstruction process can significantly reduce bias and noise in images. This was based on comparisons of the MAP-based reconstructions to those produced by other reconstruction methods, including filtered-back-projection and standard expectation-maximization (EM).
Traditionally, imaging modalities such as CT, MR, SPECT and PET, each with their own individual historical development, have contributed separately, but often in a complimentary way, to the overall diagnosis of pathological conditions. With the introduction of PACS (Picture Archiving and Communication Systems), routine access to image data from two or more of these modalities has become possible. The potential to combine functional and anatomical images is a powerful one and, as discussed, there has been significant progress in the development of multi-modality image co-registration and alignment techniques. However, with the exception of the brain, the re-alignment of images from different modalities is not straightforward, even when surface markers or reference points are used.
Other devices have been produced to perform two independent imaging procedures on a patient. Typical of the art is the device disclosed in the U.S. Pat. No. 6,205,347 B1, issued to H. T. Morgan et al., on Mar. 20, 2001. Morgan et al., disclose a multi-modality diagnostic imaging system including first and second imaging subsystems. These subsystems are described as being a CT system and a nuclear medicine system (NUC), namely SPECT. The first and second subsystems are provided for performing first and second imaging procedures, respectively, on a subject, and are remote from one another.